{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T15:25:16Z","timestamp":1787066716490,"version":"build-2736575974"},"reference-count":30,"publisher":"Public Library of Science (PLoS)","issue":"6","license":[{"start":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T00:00:00Z","timestamp":1656028800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003699","name":"Korea Railroad Research Institute","doi-asserted-by":"publisher","award":["PK2202D1"],"award-info":[{"award-number":["PK2202D1"]}],"id":[{"id":"10.13039\/501100003699","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"<jats:p>Smart card data are widely used in generating the origin and destination (O\u2013D) matrix for public transit, which contains important information for transportation planning and operation. However, the generation of the O\u2013D matrix is limited by the smart card data information that includes the boarding (origin) information without the alighting (destination) information. To solve this problem, trip chain methods have been proposed, thereby greatly contributing in estimating the destination using the smart card data. Nevertheless, unlinked trips, that is, trips with unknown destinations, are a persisting issue. The purpose of this study is to develop a method for estimating the destination of unlinked trips, in which trip chain methods cannot be applied, using temporal travel patterns and historical boarding records of the passengers based on long-term smart card data. The passengers were clustered by k-means clustering, and the time-of-day travel patterns were estimated for each cluster using a Gaussian mixture model. The travel patterns were formulated to estimate the destination of the passengers from the smart card data. The proposed method was verified using the 2018 smart card data collected in Sejong City, South Korea. The existing trip chain method matched the destinations of 60.0% of the total trips, whereas the proposed method improved the matching to 74.9% by additionally matching the destinations of 37.2% of the unlinked trips.<\/jats:p>","DOI":"10.1371\/journal.pone.0270346","type":"journal-article","created":{"date-parts":[[2022,6,24]],"date-time":"2022-06-24T17:37:43Z","timestamp":1656092263000},"page":"e0270346","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":17,"title":["Travel pattern-based bus trip origin-destination estimation using smart card data"],"prefix":"10.1371","volume":"17","author":[{"given":"Inmook","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6499-1497","authenticated-orcid":true,"given":"Shin-Hyung","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyoungtae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung-Young","family":"Kho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0746-3043","authenticated-orcid":true,"given":"Dong-Kyu","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2022,6,24]]},"reference":[{"key":"pone.0270346.ref001","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1680\/muen.2004.157.1.39","article-title":"What role for smart-card data from bus systems?.","volume":"157","author":"M Bagchi","year":"2004","journal-title":"Municipal Engineer."},{"issue":"5","key":"pone.0270346.ref002","doi-asserted-by":"crossref","first-page":"464","DOI":"10.1016\/j.tranpol.2005.06.008","article-title":"The potential of public transport smart card data.","volume":"12","author":"M Bagchi","year":"2005","journal-title":"Transport Policy"},{"issue":"3","key":"pone.0270346.ref003","doi-asserted-by":"crossref","first-page":"712","DOI":"10.1109\/TITS.2016.2600515","article-title":"Clustering Smart Card Data for Urban Mobility Analysis","volume":"18","author":"MK El Mahrsi","year":"2017","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"pone.0270346.ref004","doi-asserted-by":"crossref","first-page":"183","DOI":"10.3141\/1817-24","article-title":"Origin and destination estimation in New York City with automated fare system data","volume":"1817","author":"J Barry","year":"2002","journal-title":"Transportation Research Record"},{"key":"pone.0270346.ref005","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/15472450601122256","article-title":"Individual trip destination estimation in a transit smart card automated fare collection system","volume":"11","author":"M Tr\u00e9panier","year":"2007","journal-title":"Journal of Intelligent Transportation Systems"},{"key":"pone.0270346.ref006","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.trc.2012.01.007","article-title":"Estimation of a disaggregate multimodal public transport origin\u2013destination matrix from passive smart card data from Santiago, Chile","volume":"24","author":"MA Munizaga","year":"2012","journal-title":"Transportation Research Part C: Emerging Technologies"},{"key":"pone.0270346.ref007","unstructured":"Cui A, Bus Passenger Origin\u2013Destination Matrix Estimation using Automated Data Collection Systems. 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